To effectively suppress noise in intravoxel incoherent motion (IVIM) images while preserving structural information without the need for clean labels or repetitive acquisitions, this paper proposes a self-supervised denoising model based on similarity-constrained masking. Built upon the blind-spot denoising framework, the model learns from a single noisy image by integrating image neighborhood similarity with an adaptive masking strategy. In simulation experiments, at a signal-to-noise ratio(SNR) of 30, the proposed method reduces the signal mean square error (MSE) by 36.92%. Furthermore, the root mean square error (RMSE) of the parameters F, D and D_star decreases by 4.39%, 11.43% and 3.29%, respectively, while their structural similarity index measure (SSIM) increases by 2.73%, 0.21% and 3.37%. In real-world experiments, the SNR improvement ratios in gray matter (GM) and white matter (WM) regions increase by 0.40% and 3.85%, respectively. Clinical case analysis further demonstrates that the contrast-to-noise ratio of D and D_star values in the meningeal region improves from 0.064 and 0.005 to 0.271 and 0.026, respectively. Experimental results indicate that this method consistently achieves the highest and most stable contrast-to-noise ratio levels across all parameters. The proposed model effectively reduces noise while preserving tissue structure and parametric information, providing an efficient self-supervised solution for high-quality IVIM imaging and its clinical applications.